OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of the quotation corpus of the Russian Wiktionary
Venue: arXiv cs.CL 2020
Year: 2020

The quantitative evaluation of quotations in the Russian Wiktionary was performed using the developed Wiktionary parser. It was found that the number of quotations in the dictionary is growing fast (51.5 thousands in 2011, 62 thousands in 2012). These quotations were extracted and saved in the relational database of a machine-readable dictionary. For this database, tables related to the quotations were designed. A histogram of distribution of quotations of literary works written in different years was built. It was made an attempt to explain the characteristics of the histogram by associating it with the years of the most popular and cited (in the Russian Wiktionary) writers of the nineteenth century. It was found that more than one-third of all the quotations (the example sentences) contained in the Russian Wiktionary are taken by the editors of a Wiktionary entry from the Russian National Corpus.
Conversations with Documents. An Exploration of Document-Centered Assistance
Venue: arXiv cs.CL 2020
Year: 2020

The role of conversational assistants has become more prevalent in helping people increase their productivity. Document-centered assistance, for example to help an individual quickly review a document, has seen less significant progress, even though it has the potential to tremendously increase a user's productivity. This type of document-centered assistance is the focus of this paper. Our contributions are three-fold: (1) We first present a survey to understand the space of document-centered assistance and the capabilities people expect in this scenario. (2) We investigate the types of queries that users will pose while seeking assistance with documents, and show that document-centered questions form the majority of these queries. (3) We present a set of initial machine learned models that show that (a) we can accurately detect document-centered questions, and (b) we can build reasonably accurate models for answering such questions. These positive results are encouraging, and suggest that even greater results may be attained with continued study of this interesting and novel problem space. Our findings have implications for the design of intelligent systems to support task completion via natural interactions with documents.
FastWordBug: A Fast Method To Generate Adversarial Text Against NLP Applications
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we present a novel algorithm, FastWordBug, to efficiently generate small text perturbations in a black-box setting that forces a sentiment analysis or text classification mode to make an incorrect prediction. By combining the part of speech attributes of words, we propose a scoring method that can quickly identify important words that affect text classification. We evaluate FastWordBug on three real-world text datasets and two state-of-the-art machine learning models under black-box setting. The results show that our method can significantly reduce the accuracy of the model, and at the same time, we can call the model as little as possible, with the highest attack efficiency. We also attack two popular real-world cloud services of NLP, and the results show that our method works as well.
Modeling ASR Ambiguity for Dialogue State Tracking Using Word Confusion Networks
Venue: arXiv cs.CL 2020
Year: 2020

Spoken dialogue systems typically use a list of top-N ASR hypotheses for inferring the semantic meaning and tracking the state of the dialogue. However ASR graphs, such as confusion networks (confnets), provide a compact representation of a richer hypothesis space than a top-N ASR list. In this paper, we study the benefits of using confusion networks with a state-of-the-art neural dialogue state tracker (DST). We encode the 2-dimensional confnet into a 1-dimensional sequence of embeddings using an attentional confusion network encoder which can be used with any DST system. Our confnet encoder is plugged into the state-of-the-art 'Global-locally Self-Attentive Dialogue State Tacker' (GLAD) model for DST and obtains significant improvements in both accuracy and inference time compared to using top-N ASR hypotheses.
Analyzing the evolution and diversity of SBES Program Committee
Venue: arXiv cs.SE 2020
Year: 2020

The Brazilian Symposium on Software Engineering (SBES) is one of the most important Latin American Software Engineering conferences. It was first held in 1987, and in 2019 marks its 33rd edition. Over these years, many researchers have participated in SBES, attending the conference, submitting, and reviewing papers. The researchers who participate in the Program Committee (PC) and perform the reviewers' role are fundamentally important to SBES, since their evaluations (e.g., deciding whether a paper is accepted or not) have the potential of drawing what SBES is now. Knowing that diversity is an important aspect of any group work, we wanted to understand diversity in the SBES PC community. We investigated a number of characteristics of SBES PC members, including their gender and geographic location. We also analyzed the turnover and renovation of the committee. Among the findings, we observed that although the number of participants in the SBES PC has increased over the years, most of them are men (~80%) and from the Southeast and Northeast of Brazil, with very few members from the North region. We also observed that there is a small turnover: during the 2010 decade, only 11% of new members were added to the PC. Finally, we investigated the participation of the PC members publishing papers at SBES. We observed that only 24% of the papers accepted to SBES were authored by members who were not committee members of the respective year. Moreover, committee members usually do not collaborate among themselves: a significant number of the papers are authored by the PC members and students. This paper may contribute to the SBES community, in particular, its special interest group, in understanding the needs and challenges of the PC's participants.
Treating for-Loops as First-Class Citizens in Proofs
Venue: arXiv cs.PL 2020
Year: 2020

Indexed loop scopes have been shown to be a helpful tool in creating sound loop invariant rules in dynamic logic for programming languages with abrupt completion, such as Java. These rules do not require program transformation of the loop body, as other approaches to dealing with abrupt completion do. However, indexed loop scopes were designed specifically to provide a loop invariant rule for while loops and work rather opaquely. Here we propose replacing indexed loop scopes with a more transparent solution, which also lets us extend this idea from while loops to for loops. We further present sound loop unrolling rules for while, do and for loops, which require neither program transformation of the loop body, nor the use of nested modalities. This approach allows for loops to be treated as first-class citizens in proofs -- rather than the usual approach of transforming for loops into while loops -- which makes semi-automated proofs more transparent and easier to follow for the user, whose interactions may be required in order to close the proofs.
Explainable Subgraphs with Surprising Densities: A Subgroup Discovery Approach
Venue: arXiv cs.SI 2020
Year: 2020

The connectivity structure of graphs is typically related to the attributes of the nodes. In social networks for example, the probability of a friendship between two people depends on their attributes, such as their age, address, and hobbies. The connectivity of a graph can thus possibly be understood in terms of patterns of the form 'the subgroup of individuals with properties X are often (or rarely) friends with individuals in another subgroup with properties Y'. Such rules present potentially actionable and generalizable insights into the graph. We present a method that finds pairs of node subgroups between which the edge density is interestingly high or low, using an information-theoretic definition of interestingness. This interestingness is quantified subjectively, to contrast with prior information an analyst may have about the graph. This view immediately enables iterative mining of such patterns. Our work generalizes prior work on dense subgraph mining (i.e. subgraphs induced by a single subgroup). Moreover, not only is the proposed method more general, we also demonstrate considerable practical advantages for the single subgroup special case.
Faster Fully Dynamic Transitive Closure in Practice
Venue: arXiv cs.DS 2020
Year: 2020

The fully dynamic transitive closure problem asks to maintain reachability information in a directed graph between arbitrary pairs of vertices, while the graph undergoes a sequence of edge insertions and deletions. The problem has been thoroughly investigated in theory and many specialized algorithms for solving it have been proposed in the last decades. In two large studies [Frigioni ea, 2001; Krommidas and Zaroliagis, 2008], a number of these algorithms have been evaluated experimentally against simple static algorithms for graph traversal, showing the competitiveness and even superiority of the simple algorithms in practice, except for very dense random graphs or very high ratios of queries. A major drawback of those studies is that only small and mostly randomly generated graphs are considered. In this paper, we engineer new algorithms to maintain all-pairs reachability information which are simple and space-efficient. Moreover, we perform an extensive experimental evaluation on both generated and real-world instances that are several orders of magnitude larger than those in the previous studies. Our results indicate that our new algorithms outperform all state-of-the-art algorithms on all types of input considerably in practice.
Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository
Venue: arXiv cs.SI 2020
Year: 2020

Nowadays, Internet is a primary source of attaining health information. Massive fake health news which is spreading over the Internet, has become a severe threat to public health. Numerous studies and research works have been done in fake news detection domain, however, few of them are designed to cope with the challenges in health news. For instance, the development of explainable is required for fake health news detection. To mitigate these problems, we construct a comprehensive repository, FakeHealth, which includes news contents with rich features, news reviews with detailed explanations, social engagements and a user-user social network. Moreover, exploratory analyses are conducted to understand the characteristics of the datasets, analyze useful patterns and validate the quality of the datasets for health fake news detection. We also discuss the novel and potential future research directions for the health fake news detection.
Artificial Benchmark for Community Detection (ABCD): Fast Random Graph Model with Community Structure
Venue: arXiv cs.SI 2020
Year: 2020

Most of the current complex networks that are of interest to practitioners possess a certain community structure that plays an important role in understanding the properties of these networks. Moreover, many machine learning algorithms and tools that are developed for complex networks try to take advantage of the existence of communities to improve their performance or speed. As a result, there are many competing algorithms for detecting communities in large networks. Unfortunately, these algorithms are often quite sensitive and so they cannot be fine-tuned for a given, but a constantly changing, real-world network at hand. It is therefore important to test these algorithms for various scenarios that can only be done using synthetic graphs that have built-in community structure, power-law degree distribution, and other typical properties observed in complex networks. The standard and extensively used method for generating artificial networks is the LFR graph generator. Unfortunately, this model has some scalability limitations and it is challenging to analyze it theoretically. Finally, the mixing parameter $μ$, the main parameter of the model guiding the strength of the communities, has a non-obvious interpretation and so can lead to unnaturally-defined networks. In this paper, we provide an alternative random graph model with community structure and power-law distribution for both degrees and community sizes, the Artificial Benchmark for Community Detection (ABCD). We show that the new model solves the three issues identified above and more. The conclusion is that these models produce comparable graphs but ABCD is fast, simple, and can be easily tuned to allow the user to make a smooth transition between the two extremes: pure (independent) communities and random graph with no community structure.
Using Sampled Network Data With The Autologistic Actor Attribute Model
Venue: arXiv cs.SI 2020
Year: 2020

Social science research increasingly benefits from statistical methods for understanding the structured nature of social life, including for social network data. However, the application of statistical network models within large-scale community research is hindered by too little understanding of the validity of their inferences under realistic data collection conditions, including sampled or missing network data. The autologistic actor attribute model (ALAAM) is a statistical model based on the well-established exponential random graph model (ERGM) for social networks. ALAAMs can be regarded as a social influence model, predicting an individual-level outcome based on the actor's network ties, concurrent outcomes of his/her network partners, and attributes of the actor and his/her network partners. In particular, an ALAAM can be used to measure contagion effects, that is, the propensity of two actors connected by a social network tie to both have the same value of an attribute. We investigate the effect of using simple random samples and snowball samples of network data on ALAAM parameter inference, and find that parameter inference can still work well even with a nontrivial fraction of missing nodes. However it is safer to take a snowball sample of the network and estimate conditional on the snowball sampling structure.
Torch-Struct: Deep Structured Prediction Library
Venue: arXiv cs.CL 2020
Year: 2020

The literature on structured prediction for NLP describes a rich collection of distributions and algorithms over sequences, segmentations, alignments, and trees; however, these algorithms are difficult to utilize in deep learning frameworks. We introduce Torch-Struct, a library for structured prediction designed to take advantage of and integrate with vectorized, auto-differentiation based frameworks. Torch-Struct includes a broad collection of probabilistic structures accessed through a simple and flexible distribution-based API that connects to any deep learning model. The library utilizes batched, vectorized operations and exploits auto-differentiation to produce readable, fast, and testable code. Internally, we also include a number of general-purpose optimizations to provide cross-algorithm efficiency. Experiments show significant performance gains over fast baselines and case-studies demonstrate the benefits of the library. Torch-Struct is available at https://github.com/harvardnlp/pytorch-struct.
Isness: Using Multi-Person VR to Design Peak Mystical-Type Experiences Comparable to Psychedelics
Venue: arXiv cs.HC 2020
Year: 2020

Studies combining psychotherapy with psychedelic drugs (PsiDs) have demonstrated positive outcomes that are often associated with PsiDs' ability to induce 'mystical-type' experiences (MTEs) - i.e., subjective experiences whose characteristics include a sense of connectedness, transcendence, and ineffability. We suggest that both PsiDs and virtual reality can be situated on a broader spectrum of psychedelic technologies. To test this hypothesis, we used concepts, methods, and analysis strategies from PsiD research to design and evaluate 'Isness', a multi-person VR journey where participants experience the collective emergence, fluctuation, and dissipation of their bodies as energetic essences. A study (N=57) analyzing participant responses to a commonly used PsiD experience questionnaire (MEQ30) indicates that Isness participants had MTEs comparable to those reported in double-blind clinical studies after high doses of psilocybin & LSD. Within a supportive setting and conceptual framework, VR phenomenology can create the conditions for MTEs from which participants derive insight and meaning.
Aesthetic Quality Assessment for Group photograph
Venue: arXiv cs.CV 2020
Year: 2020

Image aesthetic quality assessment has got much attention in recent years, but not many works have been done on a specific genre of photos: Group photograph. In this work, we designed a set of high-level features based on the experience and principles of group photography: Opened-eye, Gaze, Smile, Occluded faces, Face Orientation, Facial blur, Character center. Then we combined them and 83 generic aesthetic features to build two aesthetic assessment models. We also constructed a large dataset of group photographs - GPD- annotated with the aesthetic score. The experimental result shows that our features perform well for categorizing professional photos and snapshots and predicting the distinction of multiple group photographs of diverse human states under the same scene.
Distributed Resource Allocation for Network Slicing of Bandwidth and Computational Resource
Venue: arXiv cs.NI 2020
Year: 2020

Network slicing has been considered as one of the key enablers for 5G to support diversified services and application scenarios. This paper studies the distributed network slicing utilizing both the spectrum resource offered by communication network and computational resources of a coexisting fog computing network. We propose a novel distributed framework based on a new control plane entity, regional orchestrator (RO), which can be deployed between base stations (BSs) and fog nodes to coordinate and control their bandwidth and computational resources. We propose a distributed resource allocation algorithm based on Alternating Direction Method of Multipliers with Partial Variable Splitting (DistADMM-PVS). We prove that the proposed algorithm can minimize the average latency of the entire network and at the same time guarantee satisfactory latency performance for every supported type of service. Simulation results show that the proposed algorithm converges much faster than some other existing algorithms. The joint network slicing with both bandwidth and computational resources can offer around 15% overall latency reduction compared to network slicing with only a single resource.
A reactive algorithm for deducing nodal forwarding behavior in a multihop ad-hoc wireless network in the presence of errors
Venue: arXiv cs.NI 2020
Year: 2020

novel algorithm is presented to deduce individual nodal forwarding behavior from standard end-to-end acknowledgments. The algorithm is based on a well-established mathematical method and is robust to network related errors and nodal behavior changes. The proposed solution was verified in a network simulation, during which it achieved sound results in a challenging multihop ad-hoc network environment.
Classification of Upper Limb Movements \newline Using Convolutional Neural Network \newline with 3D Inception Block
Venue: arXiv cs.HC 2020
Year: 2020

A brain-machine interface (BMI) based on electroencephalography (EEG) can overcome the movement deficits for patients and real-world applications for healthy people. Ideally, the BMI system detects user movement intentions transforms them into a control signal for a robotic arm movement. In this study, we made progress toward user intention decoding and successfully classified six different reaching movements of the right arm in the movement execution (ME). Notably, we designed an experimental environment using robotic arm movement and proposed a convolutional neural network architecture (CNN) with inception block for robust classify executed movements of the same limb. As a result, we confirmed the classification accuracies of six different directions show 0.45 for the executed session. The results proved that the proposed architecture has approximately 6~13% performance increase compared to its conventional classification models. Hence, we demonstrate the 3D inception CNN architecture to contribute to the continuous decoding of ME.
Using Positional Sequence Patterns to Estimate the Selectivity of SQL LIKE Queries
Venue: arXiv cs.DB 2020
Year: 2020

With the dramatic increase in the amount of the text-based data which commonly contains misspellings and other errors, querying such data with flexible search patterns becomes more and more commonplace. Relational databases support the LIKE operator to allow searching with a particular wildcard predicate (e.g., LIKE 'Sub%', which matches all strings starting with 'Sub'). Due to the large size of text data, executing such queries in the most optimal way is quite critical for database performance. While building the most efficient execution plan for a LIKE query, the query optimizer requires the selectivity estimate for the flexible pattern-based query predicate. Recently, SPH algorithm is proposed which employs a sequence pattern-based histogram structure to estimate the selectivity of LIKE queries. A drawback of the SPH approach is that it often overestimates the selectivity of queries. In order to alleviate the overestimation problem, in this paper, we propose a novel sequence pattern type, called positional sequence patterns. The proposed patterns differentiate between sequence item pairs that appear next to each other in all pattern occurrences from those that may have other items between them. Besides, we employ redundant pattern elimination based on pattern information content during histogram construction. Finally, we propose a partitioning-based matching scheme during the selectivity estimation. The experimental results on a real dataset from DBLP show that the proposed approach outperforms the state of the art by around 20% improvement in error rates.
Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we suggest a new neural network architecture for vanishing point detection in images. The key element is the use of the direct and transposed Fast Hough Transforms separated by convolutional layer blocks with standard activation functions. It allows us to get the answer in the coordinates of the input image at the output of the network and thus to calculate the coordinates of the vanishing point by simply selecting the maximum. Besides, it was proved that calculation of the transposed Fast Hough Transform can be performed using the direct one. The use of integral operators enables the neural network to rely on global rectilinear features in the image, and so it is ideal for detecting vanishing points. To demonstrate the effectiveness of the proposed architecture, we use a set of images from a DVR and show its superiority over existing methods. Note, in addition, that the proposed neural network architecture essentially repeats the process of direct and back projection used, for example, in computed tomography.